Gastos Casa
17 June, 2024
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 23/4/2024 | Enceres | 7880 | Andrés | maacarillas 50 unidades |
| 23/4/2024 | prestamo | 242000 | Tami | NA |
| 27/4/2024 | Comida | 41406 | Tami | Supermercado |
| 27/4/2024 | Préstamo Andrés | 122000 | Tami | NA |
| 29/4/2024 | Diosi | 45600 | Andrés | arena diosi |
| 26/4/2024 | Electricidad | 56349 | Andrés | enel |
| 4/5/2024 | Comida | 48572 | Tami | Supermercado |
| 5/4/2024 | Comida | 21700 | Andrés | carls jr |
| 5/5/2024 | Diosi | 126286 | Andrés | arenero |
| 4/5/2024 | Comida | 21600 | Andrés | carls jr |
| 5/5/2024 | Agua | 16101 | Andrés | PAC AGUAS ANDIN 000000005687837 |
| 11/5/2024 | Comida | 72079 | Tami | Supermercado |
| 13/5/2024 | Comida | 28480 | Andrés | piwen |
| 13/5/2024 | Enceres | 40000 | Andrés | mantencion toyotomi |
| 17/5/2024 | Comida | 59132 | Tami | Supermercado |
| 18/5/2024 | Gas | 94000 | Andrés | 2 lks mas |
| 24/5/2024 | Comida | 22384 | Tami | Barritas wild soul |
| 26/5/2024 | Comida | 53958 | Tami | Supermercado |
| 28/5/2024 | cartero | 4000 | Andrés | NA |
| 29/5/2024 | Electricidad | 35206 | Andrés | NA |
| 1/6/2024 | Comida | 68402 | Tami | NA |
| 1/6/2024 | Comida | 11560 | Andrés | lider gastos |
| 1/6/2024 | Comida | 16500 | Andrés | teteria |
| 2/6/2024 | Parafina | 39138 | Tami | NA |
| 4/6/2024 | Comida | 6780 | Andrés | avena |
| 9/6/2024 | Comida | 67000 | Tami | Supermercado |
| 15/6/2024 | Gas | 17550 | Andrés | NA |
| 15/6/2024 | Comida | 6530 | Tami | Cus cus y orégano |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 7.9664e+08 2 7.1918 8e-04 ***
## lag_depvar 1.2024e+11 1 2170.9389 <2e-16 ***
## Residuals 3.9711e+10 717
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 567.6095 13890.07 0.0296201
## 2-0 29836.848 23811.3117 35862.38 0.0000000
## 2-1 22608.009 19084.5900 26131.43 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
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## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
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## 33 31622.57 0 27223.29
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## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
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## 350 27574.00 2 28877.86
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## 355 39139.71 2 34030.00
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## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
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## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
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## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
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## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
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## 436 31630.43 2 29513.29
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## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
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## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
## 479 46141.14 2 42898.00
## 480 34022.57 2 46141.14
## 481 26651.86 2 34022.57
## 482 28791.86 2 26651.86
## 483 31879.00 2 28791.86
## 484 33584.71 2 31879.00
## 485 34690.43 2 33584.71
## 486 27410.43 2 34690.43
## 487 41755.00 2 27410.43
## 488 49379.57 2 41755.00
## 489 57198.86 2 49379.57
## 490 51144.57 2 57198.86
## 491 56677.43 2 51144.57
## 492 65416.43 2 56677.43
## 493 69779.71 2 65416.43
## 494 54046.00 2 69779.71
## 495 43259.57 2 54046.00
## 496 40998.57 2 43259.57
## 497 41368.57 2 40998.57
## 498 42274.29 2 41368.57
## 499 35962.71 2 42274.29
## 500 38709.00 2 35962.71
## 501 44778.14 2 38709.00
## 502 51282.43 2 44778.14
## 503 52094.86 2 51282.43
## 504 52221.43 2 52094.86
## 505 45011.43 2 52221.43
## 506 46545.43 2 45011.43
## 507 42263.00 2 46545.43
## 508 45417.43 2 42263.00
## 509 45034.71 2 45417.43
## 510 37840.57 2 45034.71
## 511 39135.43 2 37840.57
## 512 38191.14 2 39135.43
## 513 39456.86 2 38191.14
## 514 42479.14 2 39456.86
## 515 34282.57 2 42479.14
## 516 28878.43 2 34282.57
## 517 56227.14 2 28878.43
## 518 65569.43 2 56227.14
## 519 69751.29 2 65569.43
## 520 62171.71 2 69751.29
## 521 63705.14 2 62171.71
## 522 79257.86 2 63705.14
## 523 87244.71 2 79257.86
## 524 58568.00 2 87244.71
## 525 52695.29 2 58568.00
## 526 48911.00 2 52695.29
## 527 53924.00 2 48911.00
## 528 53358.86 2 53924.00
## 529 42121.14 2 53358.86
## 530 47835.71 2 42121.14
## 531 62329.29 2 47835.71
## 532 56056.86 2 62329.29
## 533 59946.43 2 56056.86
## 534 64511.57 2 59946.43
## 535 61137.43 2 64511.57
## 536 55448.71 2 61137.43
## 537 47964.43 2 55448.71
## 538 46425.71 2 47964.43
## 539 55512.00 2 46425.71
## 540 55226.29 2 55512.00
## 541 46709.14 2 55226.29
## 542 49254.71 2 46709.14
## 543 49056.29 2 49254.71
## 544 49850.57 2 49056.29
## 545 39145.71 2 49850.57
## 546 29799.43 2 39145.71
## 547 34769.86 2 29799.43
## 548 44061.57 2 34769.86
## 549 43829.14 2 44061.57
## 550 45782.00 2 43829.14
## 551 38924.57 2 45782.00
## 552 49242.43 2 38924.57
## 553 50565.00 2 49242.43
## 554 38864.43 2 50565.00
## 555 49786.71 2 38864.43
## 556 58787.86 2 49786.71
## 557 58060.86 2 58787.86
## 558 62179.43 2 58060.86
## 559 57333.86 2 62179.43
## 560 70797.00 2 57333.86
## 561 89901.71 2 70797.00
## 562 78558.14 2 89901.71
## 563 65466.00 2 78558.14
## 564 70525.00 2 65466.00
## 565 68377.86 2 70525.00
## 566 69736.29 2 68377.86
## 567 60085.86 2 69736.29
## 568 41757.00 2 60085.86
## 569 49780.29 2 41757.00
## 570 56540.29 2 49780.29
## 571 57894.29 2 56540.29
## 572 60270.29 2 57894.29
## 573 61011.00 2 60270.29
## 574 57721.43 2 61011.00
## 575 71741.00 2 57721.43
## 576 59576.00 2 71741.00
## 577 52390.29 2 59576.00
## 578 61092.29 2 52390.29
## 579 62814.00 2 61092.29
## 580 54908.29 2 62814.00
## 581 62082.00 2 54908.29
## 582 57017.71 2 62082.00
## 583 53634.43 2 57017.71
## 584 69169.00 2 53634.43
## 585 52488.14 2 69169.00
## 586 60895.57 2 52488.14
## 587 59856.57 2 60895.57
## 588 52670.00 2 59856.57
## 589 51874.57 2 52670.00
## 590 52190.57 2 51874.57
## 591 41562.43 2 52190.57
## 592 44764.14 2 41562.43
## 593 38612.71 2 44764.14
## 594 43473.14 2 38612.71
## 595 53505.00 2 43473.14
## 596 45870.86 2 53505.00
## 597 52578.00 2 45870.86
## 598 55300.00 2 52578.00
## 599 61789.71 2 55300.00
## 600 57391.71 2 61789.71
## 601 62902.29 2 57391.71
## 602 53250.43 2 62902.29
## 603 55402.57 2 53250.43
## 604 56291.29 2 55402.57
## 605 58933.57 2 56291.29
## 606 59590.71 2 58933.57
## 607 59065.00 2 59590.71
## 608 52399.57 2 59065.00
## 609 60483.43 2 52399.57
## 610 58262.71 2 60483.43
## 611 54939.71 2 58262.71
## 612 51169.00 2 54939.71
## 613 43113.29 2 51169.00
## 614 56289.71 2 43113.29
## 615 60739.86 2 56289.71
## 616 50363.14 2 60739.86
## 617 62270.86 2 50363.14
## 618 67061.57 2 62270.86
## 619 59609.00 2 67061.57
## 620 85054.00 2 59609.00
## 621 68023.29 2 85054.00
## 622 59242.29 2 68023.29
## 623 61535.14 2 59242.29
## 624 56215.86 2 61535.14
## 625 45152.29 2 56215.86
## 626 57409.57 2 45152.29
## 627 35151.43 2 57409.57
## 628 34991.43 2 35151.43
## 629 45944.71 2 34991.43
## 630 57944.71 2 45944.71
## 631 55706.29 2 57944.71
## 632 88593.71 2 55706.29
## 633 77359.43 2 88593.71
## 634 79878.71 2 77359.43
## 635 81753.00 2 79878.71
## 636 75716.00 2 81753.00
## 637 67381.43 2 75716.00
## 638 63528.57 2 67381.43
## 639 49682.86 2 63528.57
## 640 47815.00 2 49682.86
## 641 46546.14 2 47815.00
## 642 44808.71 2 46546.14
## 643 42959.57 2 44808.71
## 644 46023.86 2 42959.57
## 645 51309.57 2 46023.86
## 646 68447.29 2 51309.57
## 647 84959.29 2 68447.29
## 648 81666.29 2 84959.29
## 649 82700.86 2 81666.29
## 650 89422.14 2 82700.86
## 651 104812.71 2 89422.14
## 652 98812.71 2 104812.71
## 653 64779.86 2 98812.71
## 654 61862.86 2 64779.86
## 655 58376.43 2 61862.86
## 656 59503.57 2 58376.43
## 657 55429.43 2 59503.57
## 658 44454.57 2 55429.43
## 659 47184.00 2 44454.57
## 660 52126.71 2 47184.00
## 661 51202.00 2 52126.71
## 662 64437.14 2 51202.00
## 663 64297.14 2 64437.14
## 664 64628.57 2 64297.14
## 665 51413.14 2 64628.57
## 666 52969.43 2 51413.14
## 667 54135.29 2 52969.43
## 668 48799.43 2 54135.29
## 669 41907.86 2 48799.43
## 670 45382.00 2 41907.86
## 671 42633.29 2 45382.00
## 672 46624.71 2 42633.29
## 673 44051.86 2 46624.71
## 674 35852.86 2 44051.86
## 675 29737.71 2 35852.86
## 676 29734.86 2 29737.71
## 677 32881.71 2 29734.86
## 678 38298.57 2 32881.71
## 679 40886.14 2 38298.57
## 680 38601.86 2 40886.14
## 681 38628.86 2 38601.86
## 682 39142.57 2 38628.86
## 683 32666.14 2 39142.57
## 684 39911.57 2 32666.14
## 685 39336.29 2 39911.57
## 686 39678.86 2 39336.29
## 687 41963.14 2 39678.86
## 688 54220.57 2 41963.14
## 689 63901.86 2 54220.57
## 690 73116.00 2 63901.86
## 691 60863.86 2 73116.00
## 692 56293.86 2 60863.86
## 693 52725.00 2 56293.86
## 694 58625.00 2 52725.00
## 695 47513.00 2 58625.00
## 696 40300.14 2 47513.00
## 697 33312.43 2 40300.14
## 698 29556.71 2 33312.43
## 699 27816.71 2 29556.71
## 700 34120.29 2 27816.71
## 701 32132.57 2 34120.29
## 702 32902.57 2 32132.57
## 703 39694.14 2 32902.57
## 704 72501.29 2 39694.14
## 705 79551.14 2 72501.29
## 706 99637.71 2 79551.14
## 707 95424.29 2 99637.71
## 708 98395.14 2 95424.29
## 709 115594.71 2 98395.14
## 710 114267.57 2 115594.71
## 711 88353.29 2 114267.57
## 712 88750.86 2 88353.29
## 713 78835.71 2 88750.86
## 714 75519.14 2 78835.71
## 715 73202.86 2 75519.14
## 716 53433.29 2 73202.86
## 717 48165.71 2 53433.29
## 718 52163.14 2 48165.71
## 719 49306.86 2 52163.14
## 720 36846.86 2 49306.86
## 721 43220.57 2 36846.86
## 722 38952.29 2 43220.57
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 565 52071.11 16226.987
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 1983.393686 4025.163083 -522.939966 2451.189573 -2939.709290
## 7 8 9 10 11
## 529.622029 -5639.857421 -1205.752215 -3985.948536 -456.657258
## 12 13 14 15 16
## -4972.872759 -1665.790279 -955.753097 326.226075 -3282.032309
## 17 18 19 20 21
## -428.973710 -2173.795334 6556.010736 -1527.158819 -1212.786142
## 22 23 24 25 26
## 1467.413570 -1181.397628 235.062391 1700.064823 -7083.848772
## 27 28 29 30 31
## 922.745253 8179.967595 461.589120 29.895130 -2358.943261
## 32 33 34 35 36
## 1601.060858 4607.383843 1189.166130 2456.237450 -1793.132926
## 37 38 39 40 41
## 4665.158377 4337.512080 -2218.899458 -2945.606734 -1097.035767
## 42 43 44 45 46
## -10736.595041 7227.227389 2548.438991 1375.274394 8121.286128
## 47 48 49 50 51
## 750.812873 6589.814925 6808.553464 -5758.091384 -4723.053565
## 52 53 54 55 56
## -5025.919140 -7930.109663 6079.573892 -4082.220313 -4924.245236
## 57 58 59 60 61
## 3799.565275 862.848971 -48.189721 128.235176 -5007.517818
## 62 63 64 65 66
## 18086.367299 3718.046489 -3555.598172 5982.177030 7430.803554
## 67 68 69 70 71
## 14760.654504 1890.854452 -13029.053748 -1227.100172 4704.901274
## 72 73 74 75 76
## -4817.465895 -4362.122269 -10487.238274 2411.357833 -5432.484295
## 77 78 79 80 81
## 1002.271993 -6912.980706 464.134966 -2423.104254 -2767.855189
## 82 83 84 85 86
## -4012.723412 -631.989438 2229.160721 3702.628688 447.901764
## 87 88 89 90 91
## -506.707121 174.502654 4284.079058 -1151.839624 1153.706357
## 92 93 94 95 96
## -2054.608528 -1048.126599 168.076578 267.849335 -7488.126284
## 97 98 99 100 101
## 2342.446872 -8630.497992 -3017.504204 -4124.225347 -1835.049846
## 102 103 104 105 106
## -1357.351874 3089.841426 -2401.709707 2527.873061 -1199.920378
## 107 108 109 110 111
## 927.403649 2555.758963 -3165.562342 -4751.875927 -904.107137
## 112 113 114 115 116
## 1851.618447 11660.234868 -1199.540301 2698.824494 4306.053862
## 117 118 119 120 121
## 3567.004163 -1021.824657 -4654.449420 -3698.620642 2319.540168
## 122 123 124 125 126
## -1718.204088 1342.785680 8868.930726 911.269477 192.103732
## 127 128 129 130 131
## -2466.212389 2688.051205 7098.039672 1095.943480 -8419.850016
## 132 133 134 135 136
## 1766.809254 4161.993186 -3115.054680 -1396.115316 -841.723515
## 137 138 139 140 141
## -3874.199590 1164.630821 -503.945501 -2923.703156 1691.779250
## 142 143 144 145 146
## -1893.241594 -7851.157353 1972.587766 -3525.298466 2041.310354
## 147 148 149 150 151
## -297.524797 986.710119 -384.494431 1328.232308 1174.260163
## 152 153 154 155 156
## 3353.317600 -4843.751425 -1188.287821 -3254.803913 5920.541459
## 157 158 159 160 161
## 9751.901312 -3516.458821 -4874.421357 3489.663764 115.786529
## 162 163 164 165 166
## 2627.035909 -5956.039964 -6824.647736 4046.838893 17319.883119
## 167 168 169 170 171
## 3660.309585 -352.276606 -2410.820011 -1090.427798 3593.207552
## 172 173 174 175 176
## -208.220842 -8062.351625 2825.508440 4305.399979 629.534349
## 177 178 179 180 181
## 8754.159952 -9198.709756 -3484.571246 -10779.075167 -11335.093809
## 182 183 184 185 186
## 1084.296578 9165.496872 -1492.300393 5862.288270 6526.248238
## 187 188 189 190 191
## 13163.729685 8501.161408 -3963.794706 2520.264393 10421.711580
## 192 193 194 195 196
## -1550.025051 -2380.881225 -10246.867852 -6397.377639 1162.500840
## 197 198 199 200 201
## -5295.010478 -9884.107238 5248.645108 -3161.749502 -1815.576312
## 202 203 204 205 206
## -908.422673 6393.758384 9821.449541 568.410996 2910.726503
## 207 208 209 210 211
## 3093.026593 5788.300226 12859.360465 -5606.075978 -11262.778450
## 212 213 214 215 216
## -5703.224767 -10655.250506 -5195.120209 1390.846000 -13125.877395
## 217 218 219 220 221
## 16216.370021 7738.063393 1499.271142 26663.619228 12632.045567
## 222 223 224 225 226
## 7480.931833 14181.268395 -3717.392749 -1599.715671 3882.646393
## 227 228 229 230 231
## 463.025514 2830.512338 9085.911367 5942.688897 -1782.201931
## 232 233 234 235 236
## -1735.202416 9491.295883 -11408.633306 -7262.854672 -8567.571278
## 237 238 239 240 241
## -10175.152367 2955.041748 1255.809215 -8379.490527 -9109.678409
## 242 243 244 245 246
## 8938.521633 -7860.021544 2359.410976 -10405.349900 -4203.048310
## 247 248 249 250 251
## 1266.610558 868.653572 -12433.826742 3472.026207 1925.755265
## 252 253 254 255 256
## 4096.651513 2048.776321 -1231.984199 11063.715356 20861.970270
## 257 258 259 260 261
## 3271.564727 -4202.168279 4138.145571 -1659.792930 3749.247522
## 262 263 264 265 266
## -4832.812386 -10910.348604 -4805.188603 -619.738195 -5284.442467
## 267 268 269 270 271
## 8660.746456 -4350.043138 4098.490365 -2176.157575 4350.512785
## 272 273 274 275 276
## 649.554902 7243.828551 -1440.989592 11981.609004 -4580.271170
## 277 278 279 280 281
## 1694.222057 -404.244827 7809.504590 -5071.335366 -2777.707382
## 282 283 284 285 286
## -11324.551427 -2782.896962 18535.645982 7750.531531 2726.599670
## 287 288 289 290 291
## -636.226824 883.979494 6370.434062 6872.703892 -18764.770955
## 292 293 294 295 296
## -11220.418205 -8245.197012 9518.360274 2977.858633 -1256.305423
## 297 298 299 300 301
## 27321.890412 10095.236023 4954.366251 9571.227739 2927.851347
## 302 303 304 305 306
## -971.914181 7932.762849 -24244.599369 -3593.318239 -246.314000
## 307 308 309 310 311
## -7036.826845 -4063.447498 2833.080723 -9271.598310 -3337.052387
## 312 313 314 315 316
## -8293.364308 1440.587428 -3255.497208 1943.622164 -4169.210952
## 317 318 319 320 321
## 27351.152219 -732.030246 3270.771673 10813.881838 5602.711459
## 322 323 324 325 326
## 32401.240748 5234.765833 -20821.869558 1808.345429 1121.938033
## 327 328 329 330 331
## -6459.636182 -1760.066023 -33302.895966 766.811958 -2392.676941
## 332 333 334 335 336
## -173.518513 -3232.341332 4024.082789 -472.660828 -6981.281278
## 337 338 339 340 341
## -3160.260297 -2234.987973 -7719.369792 3797.322574 -1401.703324
## 342 343 344 345 346
## -1764.770355 -1018.901704 155.637881 467.487634 -1626.484487
## 347 348 349 350 351
## -9456.393179 -13246.121018 2240.638532 -4371.797339 -3709.781147
## 352 353 354 355 356
## -6031.173134 1692.436187 1342.850990 2723.619669 -3783.006983
## 357 358 359 360 361
## -541.513989 654.703612 6996.391441 281.333561 -34.681654
## 362 363 364 365 366
## 2584.727616 -2742.676808 -878.935589 -8746.657709 -4650.474279
## 367 368 369 370 371
## -6242.588757 -4988.203224 -7294.730364 4963.742779 342.997151
## 372 373 374 375 376
## 7098.468857 -7634.878702 -2280.028909 -3405.354003 -2487.380024
## 377 378 379 380 381
## -12477.592731 1856.864791 -10664.381172 5646.236305 9311.176633
## 382 383 384 385 386
## 3129.347112 -2388.631873 1606.355875 6748.667368 11429.018984
## 387 388 389 390 391
## -5763.934504 -5353.564800 -169.630815 8547.696071 1816.989225
## 392 393 394 395 396
## 11220.709640 -9860.983743 2753.663647 693.761908 539.832477
## 397 398 399 400 401
## -679.648532 -594.455443 -14522.650205 8458.127064 -1213.995965
## 402 403 404 405 406
## -1404.576035 6950.235074 -7945.104628 -1326.356900 -2557.718126
## 407 408 409 410 411
## -5844.228936 -2889.133438 -3944.139979 -8781.895437 6096.786874
## 412 413 414 415 416
## 1626.958379 -7378.695473 -7709.707290 14193.723227 3823.524527
## 417 418 419 420 421
## 4502.733261 -8021.258630 -4750.056213 -2613.798540 2807.037169
## 422 423 424 425 426
## -14012.889556 -2819.853418 -9123.308051 2978.454157 6959.473234
## 427 428 429 430 431
## 6577.842892 -3970.834816 -4113.735974 -4722.124077 -1795.968388
## 432 433 434 435 436
## -5717.051038 -6639.244856 -5970.005902 -1418.343530 -866.703366
## 437 438 439 440 441
## -4987.944734 2564.748020 4835.273949 -5046.424676 -2157.168108
## 442 443 444 445 446
## 1576.575025 -3829.655449 2837.219445 -6566.783696 -12111.752200
## 447 448 449 450 451
## -4535.109737 9620.904647 -2024.334604 4761.417235 -5847.547641
## 452 453 454 455 456
## -1112.691129 395.140680 3042.082815 -12243.022011 3366.172595
## 457 458 459 460 461
## -6689.996645 6521.034769 3032.493644 2536.080345 -3810.893254
## 462 463 464 465 466
## 2117.332083 22.971189 1822.790124 -487.668465 3381.358759
## 467 468 469 470 471
## -2601.219679 5835.145309 -6898.148278 -2938.300182 -2183.827346
## 472 473 474 475 476
## -4643.889685 3009.112163 7822.183338 -5971.085472 1514.428858
## 477 478 479 480 481
## -6143.094316 -2822.609673 2030.658282 -12901.851558 -9757.792016
## 482 483 484 485 486
## -1222.533786 7.821238 -965.048261 -1339.310393 -9578.692091
## 487 488 489 490 491
## 11082.428604 6260.819794 7464.586131 -5374.164342 5411.741426
## 492 493 494 495 496
## 9350.114403 6130.938023 -13388.615435 -10523.563040 -3425.633441
## 497 498 499 500 501
## -1093.859005 -509.178130 -7606.599683 615.970630 4302.277084
## 502 503 504 505 506
## 5540.623256 709.557163 131.218551 -7188.602145 601.211071
## 507 508 509 510 511
## -5012.204667 1857.906741 -1261.772308 -8123.850105 -586.938321
## 512 513 514 515 516
## -2654.717021 -569.685807 1354.392951 -9464.488452 -7756.811632
## 517 518 519 520 521
## 24280.849714 9893.808345 5969.757798 -5238.234876 2871.668469
## 522 523 524 525 526
## 17093.891392 11586.313643 -24020.260070 -5011.397626 -3700.176594
## 527 528 529 530 531
## 4596.288674 -318.423067 -11065.786707 4399.275921 13934.554497
## 532 533 534 535 536
## -4913.335335 4418.558084 5608.883695 -1726.241273 -4487.353967
## 537 538 539 540 541
## -7035.781982 -2080.696894 8340.666374 171.164813 -8098.075796
## 542 543 544 545 546
## 1837.461631 -569.652015 396.801810 -10997.223581 -11055.355787
## 547 548 549 550 551
## 2024.451105 7003.533942 -1290.923621 863.602000 -7688.238433
## 552 553 554 555 556
## 8579.520552 949.722067 -11898.388865 9175.989690 8700.325429
## 557 558 559 560 561
## 163.413023 4912.771720 -3506.310606 14161.130418 21584.442898
## 562 563 564 565 566
## -6335.484164 -9585.288039 6833.212698 296.588695 3518.002656
## 567 568 569 570 571
## -7311.077151 -17266.664030 6659.798764 6458.331801 1946.964624
## 572 573 574 575 576
## 3148.155873 1827.315069 -2104.942936 14768.851019 -9560.340413
## 577 578 579 580 581
## -6190.996755 8745.744770 2917.100303 -6482.472927 7550.696548
## 582 583 584 585 586
## -3737.918797 -2727.137174 15742.968718 -14416.581524 8464.123964
## 587 588 589 590 591
## 130.352429 -6154.722477 -714.665816 291.494041 -10610.828702
## 592 593 594 595 596
## 1812.477338 -7116.943819 3080.820141 8895.488200 -7442.874421
## 597 598 599 600 601
## 5888.092539 2790.587279 6918.536852 -3110.314798 6216.215927
## 602 603 604 605 606
## -8216.931864 2309.720768 1331.111374 3202.297147 1566.840020
## 607 608 609 610 611
## 470.950565 -5738.337871 8128.830804 -1105.905722 -2502.085503
## 612 613 614 615 616
## -3389.572699 -8173.597073 11992.435367 5009.946324 -9227.969418
## 617 618 619 620 621
## 11683.182644 6142.074960 -5467.197403 26444.084821 -12664.183873
## 622 623 624 625 626
## -6668.350654 3243.410213 -4065.291038 -10513.542374 11343.138120
## 627 628 629 630 631
## -21550.135106 -2397.682367 8694.428606 11190.724094 -1459.598873
## 632 633 634 635 636
## 33370.019856 -6399.301960 5867.500126 5555.907773 -2107.330971
## 637 638 639 640 641
## -5203.851860 -1825.152541 -12327.905019 -2182.419247 -1830.615447
## 642 643 644 645 646
## -2467.110137 -2808.759427 1859.949633 4486.912314 17038.435021
## 647 648 649 650 651
## 18680.762410 1060.995722 4952.764504 10776.396180 20335.191187
## 652 653 654 655 656
## 981.440820 -27845.469125 -1233.592912 -2189.063487 1963.106545
## 657 658 659 660 661
## -3089.010679 -10528.905723 1722.943980 4297.448095 -915.851121
## 662 663 664 665 666
## 13121.627360 1498.051548 1950.952221 -11552.042958 1470.713313
## 667 668 669 670 671
## 1286.246914 -5061.175350 -7323.048355 2130.620663 -3632.461001
## 672 673 674 675 676
## 2743.911143 -3292.140398 -9258.780659 -8259.996671 -2957.002010
## 677 678 679 680 681
## 192.334155 2878.795944 766.388662 -3763.023757 -1754.045288
## 682 683 684 685 686
## -1263.757765 -8185.914577 4678.838190 -2183.000619 -1341.278015
## 687 688 689 690 691
## 645.772904 10921.223007 9967.254401 10781.353552 -9465.512837
## 692 693 694 695 696
## -3404.844708 -3008.505401 5988.041572 -10243.139839 -7814.582771
## 697 698 699 700 701
## -8544.004819 -6236.773837 -4718.098793 3095.197323 -4361.860299
## 702 703 704 705 706
## -1867.204363 4256.270509 31170.652991 9755.134563 23724.841985
## 707 708 709 710 711
## 2083.141707 8709.813294 23331.697153 7081.210849 -17681.568926
## 712 713 714 715 716
## 5200.736352 -5059.362482 227.017785 788.381360 -16971.446548
## 717 718 719 720 721
## -5085.793814 3482.084544 -2842.601515 -12824.322747 4360.408553
## 722
## -5438.080499
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17285.89 20113.84 24339.08 24058.95 26396.42 23747.09 24458.57 19722.90
## 10 11 12 13 14 15 16 17
## 19461.23 16821.94 17594.16 14345.65 14396.47 15056.63 16741.75 15073.12
## 18 19 20 21 22 23 24 25
## 16100.80 15478.56 22513.16 21603.36 21086.73 22963.97 22294.51 22942.65
## 26 27 28 29 30 31 32 33
## 24776.13 18745.54 20460.03 28244.41 28301.68 27976.80 25622.22 27015.19
## 34 35 36 37 38 39 40 41
## 30832.26 31178.33 32577.99 30105.41 34105.49 37291.90 34367.89 31200.32
## 42 43 44 45 46 47 48 49
## 30055.88 20699.06 28166.99 30587.01 31668.86 38460.76 37958.76 42589.45
## 50 51 52 53 54 55 56 57
## 46797.09 39544.34 34149.49 29205.82 22396.57 28644.08 25247.82 21570.43
## 58 59 60 61 62 63 64 65
## 25949.01 27200.05 27495.05 27904.09 23802.92 40282.10 42113.60 37391.68
## 66 67 68 69 70 71 72 73
## 41570.20 46452.63 57048.72 55075.91 40418.81 37941.53 40939.04 35277.69
## 74 75 76 77 78 79 80 81
## 30760.67 21526.93 24706.77 20660.01 22731.98 17662.01 19663.82 18895.57
## 82 83 84 85 86 87 88 89
## 17929.87 16011.85 17280.98 20864.66 25252.53 26235.71 26260.50 26873.06
## 90 91 92 93 94 95 96 97
## 30970.27 29808.72 30801.32 28878.84 28084.07 28449.72 28853.55 22474.41
## 98 99 100 101 102 103 104 105
## 25469.07 18546.65 17410.51 15464.48 15762.21 16435.02 20877.42 19967.13
## 106 107 108 109 110 111 112 113
## 23454.49 23245.88 24910.67 27767.99 25283.02 21750.54 22024.10 24652.48
## 114 115 116 117 118 119 120 121
## 35443.54 33648.60 35473.66 38451.71 40394.40 38098.45 32954.48 29320.60
## 122 123 124 125 126 127 128 129
## 31389.35 29680.93 30854.50 38402.87 38047.75 37115.64 34000.38 35769.53
## 130 131 132 133 134 135 136 137
## 41130.91 40574.99 31836.19 33092.44 36260.63 32695.54 31093.72 30184.91
## 138 139 140 141 142 143 144 145
## 26765.23 28170.09 27941.27 25643.22 27653.96 26288.01 19933.41 22943.44
## 146 147 148 149 150 151 152 153
## 20784.83 23741.81 24278.15 25857.78 26038.62 27681.60 28973.54 31985.18
## 154 155 156 157 158 159 160 161
## 27486.00 26753.95 24325.74 30179.96 41536.89 39878.42 37261.19 42247.50
## 162 163 164 165 166 167 168 169
## 43646.54 47039.33 42535.93 37874.88 43263.40 59455.26 61652.42 60077.25
## 170 171 172 173 174 175 176 177
## 56924.43 55334.51 58018.79 57049.49 49393.78 52198.17 55915.47 55951.41
## 178 179 180 181 182 183 184 185
## 63032.00 53598.57 50371.50 41242.38 32838.99 36323.50 46358.59 45818.28
## 186 187 188 189 190 191 192 193
## 51730.75 57436.84 68146.84 73393.94 67131.31 67323.43 74345.88 70051.60
## 194 195 196 197 198 199 200 201
## 65604.72 54921.38 48991.93 50406.58 46031.11 38252.93 44634.18 42873.58
## 202 203 204 205 206 207 208 209
## 42513.99 42989.10 49737.12 58566.16 58198.27 59911.40 61555.99 65321.50
## 210 211 212 213 214 215 216 217
## 74723.93 66860.35 55129.37 49774.68 40831.98 37810.30 40902.88 30990.63
## 218 219 220 221 222 223 224 225
## 47849.22 55120.44 56016.24 78627.53 86071.78 88061.45 95601.39 86613.57
## 226 227 228 229 230 231 232 233
## 80652.64 80237.40 76910.06 76077.23 80782.17 82137.20 76610.35 71855.70
## 234 235 236 237 238 239 240 241
## 77471.06 64209.28 56299.71 48304.87 39973.24 44136.76 46274.92 39769.96
## 242 243 244 245 246 247 248 249
## 33492.34 43705.16 37991.02 41900.06 34216.33 32930.96 36561.49 39366.26
## 250 251 252 253 254 255 256 257
## 30257.83 36155.67 39931.35 45090.94 47790.84 47286.86 57518.03 74896.72
## 258 259 260 261 262 263 264 265
## 74713.03 68069.00 69540.79 65787.18 67223.53 61023.49 50370.76 46425.02
## 266 267 268 269 270 271 272 273
## 46633.01 42766.11 51510.61 47808.94 51927.59 50056.92 54096.73 54390.74
## 274 275 276 277 278 279 280 281
## 60367.42 58017.68 67625.13 61591.06 61799.67 60159.92 65863.91 59636.85
## 282 283 284 285 286 287 288 289
## 56223.98 45847.04 44254.64 61370.18 66862.83 67269.51 64704.59 63798.14
## 290 291 292 293 294 295 296 297
## 67772.01 71655.77 52780.99 42950.05 37001.64 47253.14 50473.02 49592.97
## 298 299 300 301 302 303 304 305
## 73625.48 79530.63 80193.77 84775.01 82985.77 78049.67 81493.03 56561.75
## 306 307 308 309 310 311 312 313
## 52848.17 52530.11 46362.30 43590.63 47169.60 39772.20 38502.94 33101.27
## 314 315 316 317 318 319 320 321
## 36860.21 36047.09 39852.64 37850.70 63462.60 61318.37 62930.98 70875.00
## 322 323 324 325 326 327 328 329
## 73246.19 98555.52 96944.16 72937.80 71743.78 70112.21 62118.35 59260.04
## 330 331 332 333 334 335 336 337
## 29411.62 33074.25 33510.80 35815.06 35160.35 40888.38 41956.71 37236.40
## 338 339 340 341 342 343 344 345
## 36456.13 36581.94 31932.53 37890.99 38549.91 38806.62 39676.50 41450.37
## 346 347 348 349 350 351 352 353
## 43260.06 43013.39 36005.69 26637.22 31945.80 30814.50 30407.32 28039.85
## 354 355 356 357 358 359 360 361
## 32687.15 36416.09 40849.58 39050.80 40302.58 42426.61 49771.95 50318.82
## 362 363 364 365 366 367 368 369
## 50519.13 52965.68 50466.08 49914.37 42609.19 39824.87 36027.63 33821.30
## 370 371 372 373 374 375 376 377
## 29905.69 37144.43 39415.96 47248.31 41260.60 40711.50 39258.67 38794.59
## 378 379 380 381 382 383 384 385
## 29723.85 34290.95 27389.48 35553.39 45816.80 49358.20 47643.22 49621.48
## 386 387 388 389 390 391 392 393
## 55799.70 65221.22 58478.28 52983.77 52714.30 60044.15 60564.00 69174.27
## 394 395 396 397 398 399 400 401
## 58353.34 59909.67 59472.74 58960.08 57457.17 56227.08 43074.87 51602.71
## 402 403 404 405 406 407 408 409
## 50609.86 49583.05 55941.25 48533.93 47849.72 46187.66 41893.99 40732.57
## 410 411 412 413 414 415 416 417
## 38809.47 32943.36 40763.18 43669.84 38377.99 33499.28 48270.90 52089.84
## 418 419 420 421 422 423 424 425
## 55992.69 48512.48 44860.51 43545.39 47107.75 35604.71 35335.74 29633.12
## 426 427 428 429 430 431 432 433
## 35185.38 43457.01 50302.83 47090.02 44178.41 41124.25 41013.19 37514.67
## 434 435 436 437 438 439 440 441
## 33679.01 30931.63 32497.13 34334.09 32352.11 37185.58 43349.42 40123.60
## 442 443 444 445 446 447 448 449
## 39831.57 42817.80 40718.07 44680.78 39959.61 31052.11 29897.38 41178.05
## 450 451 452 453 454 455 456 457
## 40861.73 46474.98 42140.41 42487.72 44097.35 47790.59 37732.83 42549.57
## 458 459 460 461 462 463 464 465
## 38003.54 45521.79 49018.21 51621.18 48372.67 50697.74 50897.92 52633.24
## 466 467 468 469 470 471 472 473
## 52134.21 55058.22 52404.43 57421.72 50726.87 48353.83 46949.46 43596.46
## 474 475 476 477 478 479 480 481
## 47327.39 54740.66 49205.00 50896.81 45720.61 44110.48 46924.42 36409.65
## 482 483 484 485 486 487 488 489
## 30014.39 31871.18 34549.76 36029.74 36989.12 30672.57 43118.75 49734.27
## 490 491 492 493 494 495 496 497
## 56518.74 51265.69 56066.31 63648.78 67434.62 53783.13 44424.20 42462.43
## 498 499 500 501 502 503 504 505
## 42783.46 43569.31 38093.03 40475.87 45741.81 51385.30 52090.21 52200.03
## 506 507 508 509 510 511 512 513
## 45944.22 47275.20 43559.52 46296.49 45964.42 39722.37 40845.86 40026.54
## 514 515 516 517 518 519 520 521
## 41124.75 43747.06 36635.24 31946.29 55675.62 63781.53 67409.95 60833.47
## 522 523 524 525 526 527 528 529
## 62163.97 75658.40 82588.26 57706.68 52611.18 49327.71 53677.28 53186.93
## 530 531 532 533 534 535 536 537
## 43436.44 48394.73 60970.19 55527.87 58902.69 62863.67 59936.07 55000.21
## 538 539 540 541 542 543 544 545
## 48506.41 47171.33 55055.12 54807.22 47417.25 49625.94 49453.77 50142.94
## 546 547 548 549 550 551 552 553
## 40854.78 32745.41 37058.04 45120.07 44918.40 46612.81 40662.91 49615.28
## 554 555 556 557 558 559 560 561
## 50762.82 40610.72 50087.53 57897.44 57266.66 60840.17 56635.87 68317.27
## 562 563 564 565 566 567 568 569
## 84893.63 75051.29 63691.79 68081.27 66218.28 67396.93 59023.66 43120.49
## 570 571 572 573 574 575 576 577
## 50081.95 55947.32 57122.13 59183.68 59826.37 56972.15 69136.34 58581.28
## 578 579 580 581 582 583 584 585
## 52346.54 59896.90 61390.76 54531.30 60755.63 56361.57 53426.03 66904.72
## 586 587 588 589 590 591 592 593
## 52431.45 59726.22 58824.72 52589.24 51899.08 52173.26 42951.67 45729.66
## 594 595 596 597 598 599 600 601
## 40392.32 44609.51 53313.73 46689.91 52509.41 54871.18 60502.03 56686.07
## 602 603 604 605 606 607 608 609
## 61467.36 53092.85 54960.17 55731.27 58023.87 58594.05 58137.91 52354.60
## 610 611 612 613 614 615 616 617
## 59368.62 57441.80 54558.57 51286.88 44297.28 55729.91 59591.11 50587.67
## 618 619 620 621 622 623 624 625
## 60919.50 65076.20 58609.92 80687.47 65910.64 58291.73 60281.15 55665.83
## 626 627 628 629 630 631 632 633
## 46066.43 56701.56 37389.11 37250.29 46753.99 57165.88 55223.69 83758.73
## 634 635 636 637 638 639 640 641
## 74011.21 76197.09 77823.33 72585.28 65353.72 62010.76 49997.42 48376.76
## 642 643 644 645 646 647 648 649
## 47275.82 45768.33 44163.91 46822.66 51408.85 66278.52 80605.29 77748.09
## 650 651 652 653 654 655 656 657
## 78645.75 84477.52 97831.27 92625.33 63096.45 60565.49 57540.46 58518.44
## 658 659 660 661 662 663 664 665
## 54983.48 45461.06 47829.27 52117.85 51315.52 62799.09 62677.62 62965.19
## 666 667 668 669 670 671 672 673
## 51498.72 52849.04 53860.60 49230.91 43251.38 46265.75 43880.80 47344.00
## 674 675 676 677 678 679 680 681
## 45111.64 37997.71 32691.86 32689.38 35419.78 40119.75 42364.88 40382.90
## 682 683 684 685 686 687 688 689
## 40406.33 40852.06 35232.73 41519.29 41020.14 41317.37 43299.35 53934.60
## 690 691 692 693 694 695 696 697
## 62334.65 70329.37 59698.70 55733.51 52636.96 57756.14 48114.73 41856.43
## 698 699 700 701 702 703 704 705
## 35793.49 32534.81 31025.09 36494.43 34769.78 35437.87 41330.63 69796.01
## 706 707 708 709 710 711 712 713
## 75912.87 93341.14 89685.33 92263.02 107186.36 106034.85 83550.12 83895.08
## 714 715 716 717 718 719 720 721
## 75292.13 72414.48 70404.73 53251.51 48681.06 52149.46 49671.18 38860.16
## 722
## 44390.37
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8292
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 7.191786 0.4891795 3.294978
## t2* 2170.938928 23.8900832 251.972551
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 2.944027 7.313139 13.72261
## 2 lag_depvar 1804.976748 2178.499552 2633.20507Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Jun 17 00:43:43 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Jun 17 00:43:50 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Jun 17 00:43:57 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Jun 17 00:44:04 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Jun 17 00:44:11 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Jun 17 00:44:18 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Jun 17 00:44:25 2024
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# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_24 %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2024","2023","2022","2021","2020"))| Item | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|
| Agua | 6.5300 | 5.195333 | 5.410333 | 5.849167 | 6.4696481 |
| Comida | 314.6342 | 366.009167 | 310.278417 | 317.896583 | 340.6547593 |
| Comunicaciones | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.0000000 |
| Electricidad | 63.0910 | 38.104750 | 47.072333 | 29.523000 | 35.9094630 |
| Enceres | 44.2176 | 18.259750 | 20.086417 | 14.801167 | 24.5740741 |
| Farmacia | 0.0000 | 4.733250 | 1.831667 | 13.996083 | 7.6883889 |
| Gas/Bencina | 42.8746 | 35.219333 | 44.325000 | 13.583667 | 29.5499444 |
| Diosi | 51.9874 | 55.804250 | 31.180667 | 52.687833 | 44.1012222 |
| donaciones/regalos | 0.0000 | 0.000000 | 0.000000 | 14.340167 | 5.0873889 |
| Electrodomésticos/ Mantención casa | 12.0000 | 0.000000 | 3.944000 | 56.595000 | 16.4716296 |
| VTR | 17.5920 | 12.829167 | 25.156667 | 19.086917 | 18.5592222 |
| Netflix | 3.3394 | 4.555500 | 7.151583 | 7.028750 | 6.3054444 |
| Otros | 0.0000 | 0.000000 | 3.151083 | 0.000000 | 0.7002407 |
| Total | 556.2662 | 540.710500 | 499.588167 | 545.388333 | 536.0714259 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
tryCatch(uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf24 <-uf24[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf24 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf24)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 47 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2382, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2024-07-09 00:04:58 sería de: 37.945 pesos// Percentil 95% más alto proyectado: 41.195,98
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 37608.45 | 37607.72 |
| Lo.80 | 37614.16 | 37612.93 |
| Point.Forecast | 37945.40 | 38726.12 |
| Hi.80 | 39776.26 | 43514.26 |
| Hi.95 | 40780.94 | 46048.95 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(0,1,1)
##
## Coefficients:
## ma1
## -0.8210
## s.e. 0.0872
##
## sigma^2 = 31404: log likelihood = -415.62
## AIC=835.25 AICc=835.45 BIC=839.53paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(0,0,1) errors
##
## Coefficients:
## ma1 intercept xreg
## 0.2345 600.4144 13.4781
## s.e. 0.1340 241.9116 7.5769
##
## sigma^2 = 29566: log likelihood = -418.72
## AIC=845.45 AICc=846.13 BIC=854.08forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 803.9700 | 730.9179 | 781.5400 |
| Lo.80 | 923.7849 | 851.1414 | 872.5926 |
| Point.Forecast | 1150.1207 | 1078.2489 | 1074.5299 |
| Hi.80 | 1376.4566 | 1342.3602 | 1323.1873 |
| Hi.95 | 1496.2715 | 1482.1722 | 1477.3248 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [4] Boom_0.9.15 scales_1.3.0 ggiraph_0.8.9
## [7] tidytext_0.4.1 DT_0.32 janitor_2.2.0
## [10] autoplotly_0.1.4 rvest_1.0.4 plotly_4.10.4
## [13] xts_0.13.2 forecast_8.21.1 wordcloud_2.6
## [16] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-11
## [19] NLP_0.2-1 tsibble_1.1.4 lubridate_1.9.3
## [22] forcats_1.0.0 dplyr_1.1.4 purrr_1.0.2
## [25] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
## [28] gsynth_1.2.1 lattice_0.20-45 GGally_2.2.1
## [31] ggplot2_3.5.0 gridExtra_2.3 plotrix_3.8-4
## [34] sparklyr_1.8.4 httr_1.4.7 readxl_1.4.3
## [37] zoo_1.8-12 stringr_1.5.1 stringi_1.8.3
## [40] data.table_1.15.0 reshape2_1.4.4 fUnitRoots_4021.80
## [43] plyr_1.8.9 readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] uuid_1.2-0 systemfonts_1.0.5 selectr_0.4-2
## [4] lazyeval_0.2.2 websocket_1.4.1 crosstalk_1.2.1
## [7] listenv_0.9.1 digest_0.6.34 foreach_1.5.2
## [10] htmltools_0.5.7 fansi_1.0.6 ggfortify_0.4.16
## [13] magrittr_2.0.3 doParallel_1.0.17 tzdb_0.4.0
## [16] globals_0.16.2 vroom_1.6.5 sandwich_3.1-0
## [19] askpass_1.2.0 timechange_0.3.0 anytime_0.3.9
## [22] tseries_0.10-55 colorspace_2.1-0 xfun_0.42
## [25] crayon_1.5.2 jsonlite_1.8.8 iterators_1.0.14
## [28] glue_1.7.0 gtable_0.3.4 car_3.1-2
## [31] quantmod_0.4.26 abind_1.4-5 mvtnorm_1.2-4
## [34] DBI_1.2.2 rngtools_1.5.2 Rcpp_1.0.12
## [37] lfe_2.9-0 viridisLite_0.4.2 xtable_1.8-4
## [40] bit_4.0.5 Formula_1.2-5 htmlwidgets_1.6.4
## [43] timeSeries_4032.109 gplots_3.1.3.1 ellipsis_0.3.2
## [46] spatial_7.3-14 farver_2.1.1 pkgconfig_2.0.3
## [49] nnet_7.3-16 sass_0.4.8 dbplyr_2.4.0
## [52] chromote_0.2.0 utf8_1.2.4 labeling_0.4.3
## [55] tidyselect_1.2.0 rlang_1.1.3 later_1.3.2
## [58] munsell_0.5.0 cellranger_1.1.0 tools_4.1.2
## [61] cachem_1.0.8 cli_3.6.2 generics_0.1.3
## [64] evaluate_0.23 fastmap_1.1.1 yaml_2.3.8
## [67] processx_3.8.3 knitr_1.45 bit64_4.0.5
## [70] caTools_1.18.2 future_1.33.1 nlme_3.1-153
## [73] doRNG_1.8.6 slam_0.1-50 xml2_1.3.6
## [76] tokenizers_0.3.0 compiler_4.1.2 rstudioapi_0.15.0
## [79] curl_5.2.0 bslib_0.6.1 highr_0.10
## [82] ps_1.7.6 fBasics_4032.96 Matrix_1.6-5
## [85] its.analysis_1.6.0 urca_1.3-3 vctrs_0.6.5
## [88] pillar_1.9.0 lifecycle_1.0.4 lmtest_0.9-40
## [91] jquerylib_0.1.4 bitops_1.0-7 R6_2.5.1
## [94] promises_1.2.1 KernSmooth_2.23-20 janeaustenr_1.0.0
## [97] parallelly_1.37.0 codetools_0.2-18 ggstats_0.5.1
## [100] assertthat_0.2.1 boot_1.3-28 gtools_3.9.5
## [103] MASS_7.3-54 openssl_2.1.1 withr_3.0.0
## [106] fracdiff_1.5-3 parallel_4.1.2 hms_1.1.3
## [109] quadprog_1.5-8 timeDate_4032.109 rmarkdown_2.25
## [112] snakecase_0.11.1 carData_3.0-5 TTR_0.24.4#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))